Application of Tribological Artificial Neural Networks in Machine Elements

• Published in: Tribology letters Vol. 71; no. 1; p. 3
• Main Authors: Walker, J., Questa, H., Raman, A., Ahmed, M., Mohammadpour, M., Bewsher, S. R., Offner, G.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: Accuracy; Artificial neural networks; Case studies; Chemistry and Materials Science; Composite materials; Corrosion and Coatings; Dynamic models; Film thickness; Friction; Lubricants & lubrication; Machine learning; Materials Science; Nanotechnology; Neural networks; Original Paper; Partial differential equations; Physical Chemistry; Roller bearings; Simulation; Spur gears; Steel; Surfaces and Interfaces; Theoretical and Applied Mechanics; Thin Films; Tribology; Viscosity; ANN; Data driven; Friction; EHL; Gear; Tribology; Bearing
• ISSN: 1023-8883; 1573-2711
• DOI: 10.1007/s11249-022-01673-5

Traditionally, analytical equations used in tribo-dynamic modelling, such as those used for predicting central film thickness within elastohydrodynamic lubricated contacts, have led to timely computations, but tend to lack the accuracy of numerical solvers. However, it can be shown that data-driven solutions, such as machine learning, can significantly improve computational efficiency of tribo-dynamic simulations of machine elements without comprising accuracy relative to the numerical solution. During this study, artificial neural networks (ANNs) are trained using data produced via numerical solutions, which are constrained by the regimes of lubrication to ensure the quality of the training data set. Multiple ANNs are then implemented to predict EHL central film thickness, as well as viscous and boundary friction, in multiple commonly used machine elements, such as a rolling element bearing and a spur gear. The viscous and boundary friction ANN prediction are compared directly against ball-on-disc experimental measurements to validate its accuracy.